STL vs MIL prediction for May 25, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects MIL 4.3 - STL 4.5. MIL is favored with a 50.6% win probability. The run line is -1.5 and the total is 7.5. Model projects 8.9 total runs.
MIL
4.3
Projected Score
VS
O/U 7.5
STL
4.5
Projected Score
Win Probability
MILSTL
-1.5
Run Line (MIL)
7.5
Total Line
10,000
Simulations
Calibrated accuracy at this confidence: 53.4% (2,300 games)
Projected Runs Range 10th – 90th percentile
STL
346
MIL
246
Projected
MIL 4.3 — STL 4.5
Actual
MIL 5 — STL 1
Starting Pitcher Matchup
Matthew Liberatore L
STL
FF32%94 mph9% whiff
SL25%86 mph34% whiff
CH16%89 mph22% whiff
Jacob Misiorowski R
MIL
FF61%100 mph40% whiff
SL24%94 mph24% whiff
CU13%87 mph39% whiff
Weather Impact
American Family Field
81°F6 mph windRoof: retractable
HR: 1.016 Total: 1.006
thin air
Bullpen Comparison
STL
4.38ERA
4.36FIP
8.15K/9
4.53BB/9
1.39WHIP
MIL
3.67ERA
3.24FIP
9.66K/9
4.27BB/9
1.34WHIP
Betting Edges
ML AWAY
+31.5% EV
+184
RUN_LINE AWAY +1.5
-27.0% EV
-120
RUN_LINE HOME -1.5
-25.3% EV
+100
ML HOME
-22.2% EV
-222
TOTAL UNDER 7.5
-14.7% EV
-110
F5_ML HOME
-14.2% EV
-233
First 5 Innings & NRFI
STL F5
2.1 runs
35.5% win
MIL F5
2.5 runs
48.3% win
F5 Total
4.6
NRFI
57.4%
YRFI
42.6%
Avg 1st Inn Runs
0.87
HR Spotlight
Avg HRs
1.9
Over 0.5 HR
85%
Over 1.5 HR
58%
No HR
15%
Andrew Vaughn MIL24.6%
ISO: 0.188 | Barrel: 7.4% | vs Matthew Liberatore | Platoon: 1.12x
Brice Turang MIL20.7%
ISO: 0.056 | Barrel: 9.8% | vs Matthew Liberatore
Jordan Walker STL18.0%
ISO: 0.278 | Barrel: 16.8% | vs Jacob Misiorowski
Pitcher Strikeout Projections
Matthew Liberatore
0.0 K projected
STL | K/9: 0.0
Jacob Misiorowski
0.0 K projected
MIL | K/9: 0.0
Injury Report
STL8 injured
Nathan Church LF10-DAY-IL
Ramon Urias 3B10-DAY-IL
Lars Nootbaar LF60-DAY-IL
Packy Naughton RPDAY-TO-DAY
Sem Robberse SPDAY-TO-DAY
Victor Santos RPDAY-TO-DAY
+2 more
MIL8 injured
Logan Henderson SPDAY-TO-DAY
Quinn Priester SP15-DAY-IL
Brandon Woodruff SP15-DAY-IL
Rob Zastryzny RP60-DAY-IL
Jared Koenig RP15-DAY-IL
Brandon Lockridge LF10-DAY-IL
+2 more
AI Intelligence Analysis
NEUTRAL -2RED ZONE44.5% WR (n=165)
DATA_INTEGRITY FAILURE: Model leans STL AWAY +31.5% edge (46.3% model prob) despite Misiorowski (A- pitcher, 0.826 grade, 13.9 K/9, 37.3% K-rate) vs Liberatore (B- pitcher, 0.448 grade, 7.6 K/9). This is bottom-tier starting pitcher vs ace — market correctly prices MIL home favorite at -222 (69% implied). Ace-at-home beats back-end arm; market has it right.
Key Factors
- Pitcher reality check: Misiorowski (A- grade, 0.826 overall_score, 13.9 K/9) is ELITE. Liberatore (B- grade, 0.448 overall_score, 7.6 K/9) is pedestrian. This is NOT a close matchup; Misiorowski outclasses.
- Market correctly priced MIL -222 (69% implied home win). Model saying STL 46.3% implies market undervaluing STL by 11.1 points — IMPLAUSIBLE when model SP analysis shows clear home ace advantage.
- RED zone + away ML: Model recommending away underdog in RED zone (44.5% WR, n=165) — historical money pit.
- Post-game validation: Brewers won 5-1 handily. Misiorowski matched career high 12 K, carried no-hitter into 6th. This was not an edge opportunity; home ace crushed weak visitor arm as expected.
Risk Factors
- Model disagreement with pitcher-driven market is core issue. When model projects 46.3% win prob AGAINST a -222 favorite, ask: 'Does my simulation know something the market doesn't?' Answer: NO. Misiorowski is clearly elite (A- grade, 13.9 K/9), Liberatore clearly weak (B- grade). Market is right.
- Recommending STL away underdog at +184 would be 'fading the ace' play — these routinely underperform.
- AWAY RED ZONE + HIGH EDGE = classic data integrity failure pattern.
PITCHER MISMATCHDATA INTEGRITYRED ZONEMODEL MARKET CONFLICTSTRONG AVOID
Edge Analysis
Moneyline
MIL 50.6%
-25.3 pts
Run Line
-1.5
-25.3 pts
Total
7.5
+5.7 pts
How this prediction was generated: This page shows output from the Olympus Bets MLB Baseball Monte Carlo engine. Each game is simulated 10,000 times using real-time team data, injury reports, and current odds. Probabilities are calibrated using Bayesian methods and sized via the Kelly Criterion. Full methodology →